Article summary: Learn how to evaluate whether an AI feature is worth building before investing engineering resources. This framework helps B2B SaaS founders assess AI product strategy across five dimensions: Customer Validation, Customer Adoption, Customer Value, Competitive Advantage, and Commercial Viability. Discover how to avoid costly AI features, improve AI adoption, protect profit margins, strengthen competitive differentiation, and build AI products that create durab

Article Summary for B2B SaaS founders: AI adoption does not automatically translate into revenue growth. Many B2B SaaS companies are investing heavily in AI features that increase infrastructure costs without creating expansion opportunities. The most successful AI businesses focus on embedding AI into mission-critical workflows that drive behaviour change, operational dependency, and long-term customer value. In an increasingly commoditized AI market, workflow ownership—not

Strategic Summary: AI is fundamentally changing SaaS economics. Traditional growth metrics like ARR are becoming increasingly incomplete because they fail to reflect operational adoption depth, infrastructure burden, and profitability at the customer level. Over the next five years, the strongest AI SaaS businesses will be those that redesign their pricing, expansion triggers, implementation strategy, and revenue architecture around usage economics rather than static subscrip

Strategic Summary for SaaS Leaders: While consumer AI usage is high, enterprise AI adoption remains low. Most SaaS organizations are currently in the "Messy Middle"—the transition from AI experiments to scaled operational integration. Success in 2026 is defined not by "isolated AI features," but by redesigning internal workflows, hardening data infrastructure, and treating AI as a horizontal system capability rather than a vertical product add-on. There is a nagging feeling